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Reinforcement learning : an introduction /

by Sutton, Richard S; Barto, Andrew G.
Series: Adaptive computation and machine learning: Publisher: Cambridge, Mass. : MIT Press, 1998Description: xviii, 322 pages : illustrations ; 24 cm.ISBN: 0262193981; 9780262193986.Subject(s): Reinforcement learning | Artificial Intelligence | Pattern Recognition, Automated | Operations Research
Contents:
Contents -- Series Foreword -- Preface -- The Problem -- Introduction -- Evaluative Feedback -- The Reinforcement Learning Problem -- Elementary Solution Methods -- Dynamic Programming -- Monte Carlo Methods -- Temporal-Difference Learning -- A Unified View -- Eligibility Traces -- Generalization and Function Approximation -- Planning and Learning -- Dimensions of Reinforcement Learning -- Case Studies -- References -- Summary of Notation -- Index.
Review: "In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability."--Jacket.
Item type Location Call number Status Date due
BOOK BOOK Mesa Lab Q325.6 .S88 1998 (Browse shelf) Checked out 11/13/2017

Includes bibliographical references (pages 291-312) and index.

Contents -- Series Foreword -- Preface -- I. The Problem -- 1. Introduction -- 2. Evaluative Feedback -- 3. The Reinforcement Learning Problem -- II. Elementary Solution Methods -- 4. Dynamic Programming -- 5. Monte Carlo Methods -- 6. Temporal-Difference Learning -- III. A Unified View -- 7. Eligibility Traces -- 8. Generalization and Function Approximation -- 9. Planning and Learning -- 10. Dimensions of Reinforcement Learning -- 11. Case Studies -- References -- Summary of Notation -- Index.

"In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability."--Jacket.

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